Overview

Dataset statistics

Number of variables31
Number of observations9256
Missing cells26568
Missing cells (%)9.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory248.0 B

Variable types

Categorical19
Numeric12

Alerts

TypeOfIncident has constant value "Special Service"Constant
IncidentNumber has a high cardinality: 9256 distinct valuesHigh cardinality
DateTimeOfCall has a high cardinality: 9243 distinct valuesHigh cardinality
FinalDescription has a high cardinality: 5606 distinct valuesHigh cardinality
PropertyType has a high cardinality: 185 distinct valuesHigh cardinality
WardCode has a high cardinality: 758 distinct valuesHigh cardinality
Ward has a high cardinality: 1182 distinct valuesHigh cardinality
Borough has a high cardinality: 70 distinct valuesHigh cardinality
StnGroundName has a high cardinality: 108 distinct valuesHigh cardinality
Street has a high cardinality: 6894 distinct valuesHigh cardinality
PostcodeDistrict has a high cardinality: 276 distinct valuesHigh cardinality
CalYear is highly overall correlated with HourlyNotionalCost(£) and 2 other fieldsHigh correlation
PumpHoursTotal is highly overall correlated with IncidentNotionalCost(£)High correlation
HourlyNotionalCost(£) is highly overall correlated with CalYear and 2 other fieldsHigh correlation
IncidentNotionalCost(£) is highly overall correlated with CalYear and 2 other fieldsHigh correlation
UPRN is highly overall correlated with BoroughCode and 1 other fieldsHigh correlation
USRN is highly overall correlated with BoroughCode and 1 other fieldsHigh correlation
Easting_m is highly overall correlated with Easting_rounded and 3 other fieldsHigh correlation
Northing_m is highly overall correlated with Northing_rounded and 3 other fieldsHigh correlation
Easting_rounded is highly overall correlated with Easting_m and 3 other fieldsHigh correlation
Northing_rounded is highly overall correlated with Northing_m and 3 other fieldsHigh correlation
Latitude is highly overall correlated with Northing_m and 1 other fieldsHigh correlation
Longitude is highly overall correlated with Easting_m and 3 other fieldsHigh correlation
FinYear is highly overall correlated with CalYear and 1 other fieldsHigh correlation
SpecialServiceTypeCategory is highly overall correlated with SpecialServiceTypeHigh correlation
SpecialServiceType is highly overall correlated with SpecialServiceTypeCategoryHigh correlation
BoroughCode is highly overall correlated with UPRN and 7 other fieldsHigh correlation
Borough is highly overall correlated with UPRN and 7 other fieldsHigh correlation
PumpCount is highly imbalanced (93.6%)Imbalance
AnimalGroupParent is highly imbalanced (52.8%)Imbalance
OriginofCall is highly imbalanced (63.0%)Imbalance
UPRN has 5849 (63.2%) missing valuesMissing
USRN has 1156 (12.5%) missing valuesMissing
Easting_m has 4830 (52.2%) missing valuesMissing
Northing_m has 4830 (52.2%) missing valuesMissing
Latitude has 4830 (52.2%) missing valuesMissing
Longitude has 4830 (52.2%) missing valuesMissing
Latitude is highly skewed (γ1 = -46.66885119)Skewed
IncidentNumber is uniformly distributedUniform
DateTimeOfCall is uniformly distributedUniform
IncidentNumber has unique valuesUnique

Reproduction

Analysis started2023-04-22 11:07:44.966953
Analysis finished2023-04-22 11:08:05.903105
Duration20.94 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

IncidentNumber
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct9256
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size72.4 KiB
139091
 
1
092437-16072019
 
1
083488-30062019
 
1
083542-30062019
 
1
083624-30062019
 
1
Other values (9251)
9251 

Length

Max length16
Median length15
Mean length12.083081
Min length4

Characters and Unicode

Total characters111841
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9256 ?
Unique (%)100.0%

Sample

1st row139091
2nd row275091
3rd row2075091
4th row2872091
5th row3553091

Common Values

ValueCountFrequency (%)
139091 1
 
< 0.1%
092437-16072019 1
 
< 0.1%
083488-30062019 1
 
< 0.1%
083542-30062019 1
 
< 0.1%
083624-30062019 1
 
< 0.1%
083667-01072019 1
 
< 0.1%
083800-01072019 1
 
< 0.1%
083855-01072019 1
 
< 0.1%
084340-02072019 1
 
< 0.1%
084464-02072019 1
 
< 0.1%
Other values (9246) 9246
99.9%

Length

2023-04-22T13:08:05.972659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
139091 1
 
< 0.1%
17262091 1
 
< 0.1%
5797091 1
 
< 0.1%
10976091 1
 
< 0.1%
4306091 1
 
< 0.1%
2075091 1
 
< 0.1%
2872091 1
 
< 0.1%
3553091 1
 
< 0.1%
3742091 1
 
< 0.1%
4011091 1
 
< 0.1%
Other values (9246) 9246
99.9%

Most occurring characters

ValueCountFrequency (%)
1 23809
21.3%
0 21356
19.1%
2 17360
15.5%
9 6515
 
5.8%
3 6442
 
5.8%
6 6424
 
5.7%
5 6369
 
5.7%
7 6257
 
5.6%
4 6198
 
5.5%
8 5918
 
5.3%
Other values (2) 5193
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 106648
95.4%
Dash Punctuation 5190
 
4.6%
Uppercase Letter 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 23809
22.3%
0 21356
20.0%
2 17360
16.3%
9 6515
 
6.1%
3 6442
 
6.0%
6 6424
 
6.0%
5 6369
 
6.0%
7 6257
 
5.9%
4 6198
 
5.8%
8 5918
 
5.5%
Dash Punctuation
ValueCountFrequency (%)
- 5190
100.0%
Uppercase Letter
ValueCountFrequency (%)
M 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 111838
> 99.9%
Latin 3
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 23809
21.3%
0 21356
19.1%
2 17360
15.5%
9 6515
 
5.8%
3 6442
 
5.8%
6 6424
 
5.7%
5 6369
 
5.7%
7 6257
 
5.6%
4 6198
 
5.5%
8 5918
 
5.3%
Latin
ValueCountFrequency (%)
M 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 111841
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 23809
21.3%
0 21356
19.1%
2 17360
15.5%
9 6515
 
5.8%
3 6442
 
5.8%
6 6424
 
5.7%
5 6369
 
5.7%
7 6257
 
5.6%
4 6198
 
5.5%
8 5918
 
5.3%
Other values (2) 5193
 
4.6%

DateTimeOfCall
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct9243
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size72.4 KiB
2009-08-22 16:26:00
 
2
2011-04-22 14:50:00
 
2
2017-04-30 14:15:00
 
2
2009-07-11 11:26:00
 
2
2014-01-23 09:17:00
 
2
Other values (9238)
9246 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters175864
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9230 ?
Unique (%)99.7%

Sample

1st row2009-01-01 03:01:00
2nd row2009-01-01 08:51:00
3rd row2009-01-04 10:07:00
4th row2009-01-05 12:27:00
5th row2009-01-06 15:23:00

Common Values

ValueCountFrequency (%)
2009-08-22 16:26:00 2
 
< 0.1%
2011-04-22 14:50:00 2
 
< 0.1%
2017-04-30 14:15:00 2
 
< 0.1%
2009-07-11 11:26:00 2
 
< 0.1%
2014-01-23 09:17:00 2
 
< 0.1%
2016-12-11 21:08:00 2
 
< 0.1%
2013-11-03 14:26:00 2
 
< 0.1%
2014-05-02 09:31:00 2
 
< 0.1%
2022-10-17 13:28:00 2
 
< 0.1%
2010-07-16 23:08:00 2
 
< 0.1%
Other values (9233) 9236
99.8%

Length

2023-04-22T13:08:06.059942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10:12:00 23
 
0.1%
12:45:00 22
 
0.1%
16:19:00 20
 
0.1%
17:07:00 19
 
0.1%
11:28:00 19
 
0.1%
13:43:00 18
 
0.1%
11:21:00 18
 
0.1%
12:38:00 18
 
0.1%
13:28:00 18
 
0.1%
14:18:00 18
 
0.1%
Other values (5435) 18319
99.0%

Most occurring characters

ValueCountFrequency (%)
0 46807
26.6%
1 24164
13.7%
2 23914
13.6%
- 18512
 
10.5%
: 18512
 
10.5%
9256
 
5.3%
3 6161
 
3.5%
5 5596
 
3.2%
4 5288
 
3.0%
9 4755
 
2.7%
Other values (3) 12899
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 129584
73.7%
Dash Punctuation 18512
 
10.5%
Other Punctuation 18512
 
10.5%
Space Separator 9256
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46807
36.1%
1 24164
18.6%
2 23914
18.5%
3 6161
 
4.8%
5 5596
 
4.3%
4 5288
 
4.1%
9 4755
 
3.7%
8 4356
 
3.4%
7 4308
 
3.3%
6 4235
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 18512
100.0%
Other Punctuation
ValueCountFrequency (%)
: 18512
100.0%
Space Separator
ValueCountFrequency (%)
9256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 175864
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46807
26.6%
1 24164
13.7%
2 23914
13.6%
- 18512
 
10.5%
: 18512
 
10.5%
9256
 
5.3%
3 6161
 
3.5%
5 5596
 
3.2%
4 5288
 
3.0%
9 4755
 
2.7%
Other values (3) 12899
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 175864
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46807
26.6%
1 24164
13.7%
2 23914
13.6%
- 18512
 
10.5%
: 18512
 
10.5%
9256
 
5.3%
3 6161
 
3.5%
5 5596
 
3.2%
4 5288
 
3.0%
9 4755
 
2.7%
Other values (3) 12899
 
7.3%

CalYear
Real number (ℝ)

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.1519
Minimum2009
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size72.4 KiB
2023-04-22T13:08:06.275428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2009
Q12012
median2016
Q32020
95-th percentile2022
Maximum2023
Range14
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.2649227
Coefficient of variation (CV)0.0021153776
Kurtosis-1.2979393
Mean2016.1519
Median Absolute Deviation (MAD)4
Skewness-0.13566023
Sum18661502
Variance18.189565
MonotonicityIncreasing
2023-04-22T13:08:06.358599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2022 1029
11.1%
2021 885
 
9.6%
2020 758
 
8.2%
2011 620
 
6.7%
2010 611
 
6.6%
2018 610
 
6.6%
2016 604
 
6.5%
2019 604
 
6.5%
2012 603
 
6.5%
2013 585
 
6.3%
Other values (5) 2347
25.4%
ValueCountFrequency (%)
2009 568
6.1%
2010 611
6.6%
2011 620
6.7%
2012 603
6.5%
2013 585
6.3%
2014 583
6.3%
2015 540
5.8%
2016 604
6.5%
2017 539
5.8%
2018 610
6.6%
ValueCountFrequency (%)
2023 117
 
1.3%
2022 1029
11.1%
2021 885
9.6%
2020 758
8.2%
2019 604
6.5%
2018 610
6.6%
2017 539
5.8%
2016 604
6.5%
2015 540
5.8%
2014 583
6.3%

FinYear
Categorical

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size72.4 KiB
2022/23
955 
2021/22
931 
2020/21
780 
2011/12
650 
2016/17
636 
Other values (10)
5304 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters64792
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2008/09
2nd row2008/09
3rd row2008/09
4th row2008/09
5th row2008/09

Common Values

ValueCountFrequency (%)
2022/23 955
10.3%
2021/22 931
10.1%
2020/21 780
 
8.4%
2011/12 650
 
7.0%
2016/17 636
 
6.9%
2019/20 620
 
6.7%
2018/19 618
 
6.7%
2013/14 593
 
6.4%
2012/13 585
 
6.3%
2010/11 583
 
6.3%
Other values (5) 2305
24.9%

Length

2023-04-22T13:08:06.447652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022/23 955
10.3%
2021/22 931
10.1%
2020/21 780
 
8.4%
2011/12 650
 
7.0%
2016/17 636
 
6.9%
2019/20 620
 
6.7%
2018/19 618
 
6.7%
2013/14 593
 
6.4%
2012/13 585
 
6.3%
2010/11 583
 
6.3%
Other values (5) 2305
24.9%

Most occurring characters

ValueCountFrequency (%)
2 18329
28.3%
1 14681
22.7%
0 12635
19.5%
/ 9256
14.3%
3 2133
 
3.3%
9 1936
 
3.0%
8 1262
 
1.9%
6 1166
 
1.8%
7 1155
 
1.8%
4 1151
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 55536
85.7%
Other Punctuation 9256
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 18329
33.0%
1 14681
26.4%
0 12635
22.8%
3 2133
 
3.8%
9 1936
 
3.5%
8 1262
 
2.3%
6 1166
 
2.1%
7 1155
 
2.1%
4 1151
 
2.1%
5 1088
 
2.0%
Other Punctuation
ValueCountFrequency (%)
/ 9256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 64792
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 18329
28.3%
1 14681
22.7%
0 12635
19.5%
/ 9256
14.3%
3 2133
 
3.3%
9 1936
 
3.0%
8 1262
 
1.9%
6 1166
 
1.8%
7 1155
 
1.8%
4 1151
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64792
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 18329
28.3%
1 14681
22.7%
0 12635
19.5%
/ 9256
14.3%
3 2133
 
3.3%
9 1936
 
3.0%
8 1262
 
1.9%
6 1166
 
1.8%
7 1155
 
1.8%
4 1151
 
1.8%

TypeOfIncident
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size72.4 KiB
Special Service
9256 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters138840
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSpecial Service
2nd rowSpecial Service
3rd rowSpecial Service
4th rowSpecial Service
5th rowSpecial Service

Common Values

ValueCountFrequency (%)
Special Service 9256
100.0%

Length

2023-04-22T13:08:06.537313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-22T13:08:06.621536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
special 9256
50.0%
service 9256
50.0%

Most occurring characters

ValueCountFrequency (%)
e 27768
20.0%
S 18512
13.3%
c 18512
13.3%
i 18512
13.3%
p 9256
 
6.7%
a 9256
 
6.7%
l 9256
 
6.7%
9256
 
6.7%
r 9256
 
6.7%
v 9256
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 111072
80.0%
Uppercase Letter 18512
 
13.3%
Space Separator 9256
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 27768
25.0%
c 18512
16.7%
i 18512
16.7%
p 9256
 
8.3%
a 9256
 
8.3%
l 9256
 
8.3%
r 9256
 
8.3%
v 9256
 
8.3%
Uppercase Letter
ValueCountFrequency (%)
S 18512
100.0%
Space Separator
ValueCountFrequency (%)
9256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 129584
93.3%
Common 9256
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 27768
21.4%
S 18512
14.3%
c 18512
14.3%
i 18512
14.3%
p 9256
 
7.1%
a 9256
 
7.1%
l 9256
 
7.1%
r 9256
 
7.1%
v 9256
 
7.1%
Common
ValueCountFrequency (%)
9256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 138840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 27768
20.0%
S 18512
13.3%
c 18512
13.3%
i 18512
13.3%
p 9256
 
6.7%
a 9256
 
6.7%
l 9256
 
6.7%
9256
 
6.7%
r 9256
 
6.7%
v 9256
 
6.7%

PumpCount
Categorical

Distinct4
Distinct (%)< 0.1%
Missing64
Missing (%)0.7%
Memory size72.4 KiB
1.0
9039 
2.0
 
142
3.0
 
10
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters27576
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 9039
97.7%
2.0 142
 
1.5%
3.0 10
 
0.1%
4.0 1
 
< 0.1%
(Missing) 64
 
0.7%

Length

2023-04-22T13:08:06.687656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-22T13:08:06.777303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 9039
98.3%
2.0 142
 
1.5%
3.0 10
 
0.1%
4.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 9192
33.3%
0 9192
33.3%
1 9039
32.8%
2 142
 
0.5%
3 10
 
< 0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18384
66.7%
Other Punctuation 9192
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9192
50.0%
1 9039
49.2%
2 142
 
0.8%
3 10
 
0.1%
4 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 9192
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27576
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9192
33.3%
0 9192
33.3%
1 9039
32.8%
2 142
 
0.5%
3 10
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9192
33.3%
0 9192
33.3%
1 9039
32.8%
2 142
 
0.5%
3 10
 
< 0.1%
4 1
 
< 0.1%

PumpHoursTotal
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing65
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean1.1765858
Minimum0
Maximum12
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size72.4 KiB
2023-04-22T13:08:06.854610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6199794
Coefficient of variation (CV)0.52693089
Kurtosis60.785028
Mean1.1765858
Median Absolute Deviation (MAD)0
Skewness6.24564
Sum10814
Variance0.38437446
MonotonicityNot monotonic
2023-04-22T13:08:06.946688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 8120
87.7%
2 748
 
8.1%
3 214
 
2.3%
4 52
 
0.6%
5 29
 
0.3%
7 7
 
0.1%
6 7
 
0.1%
9 6
 
0.1%
8 3
 
< 0.1%
12 2
 
< 0.1%
Other values (2) 3
 
< 0.1%
(Missing) 65
 
0.7%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 8120
87.7%
2 748
 
8.1%
3 214
 
2.3%
4 52
 
0.6%
5 29
 
0.3%
6 7
 
0.1%
7 7
 
0.1%
8 3
 
< 0.1%
9 6
 
0.1%
ValueCountFrequency (%)
12 2
 
< 0.1%
10 1
 
< 0.1%
9 6
 
0.1%
8 3
 
< 0.1%
7 7
 
0.1%
6 7
 
0.1%
5 29
 
0.3%
4 52
 
0.6%
3 214
 
2.3%
2 748
8.1%

HourlyNotionalCost(£)
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean311.88516
Minimum255
Maximum364
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size72.4 KiB
2023-04-22T13:08:07.037683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum255
5-th percentile260
Q1260
median326
Q3346
95-th percentile364
Maximum364
Range109
Interquartile range (IQR)86

Descriptive statistics

Standard deviation38.021091
Coefficient of variation (CV)0.12190734
Kurtosis-1.4556624
Mean311.88516
Median Absolute Deviation (MAD)28
Skewness-0.23383664
Sum2886809
Variance1445.6033
MonotonicityIncreasing
2023-04-22T13:08:07.117658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
260 2391
25.8%
364 955
 
10.3%
352 931
 
10.1%
346 780
 
8.4%
326 636
 
6.9%
339 620
 
6.7%
333 618
 
6.7%
290 593
 
6.4%
295 558
 
6.0%
298 530
 
5.7%
Other values (2) 644
 
7.0%
ValueCountFrequency (%)
255 125
 
1.4%
260 2391
25.8%
290 593
 
6.4%
295 558
 
6.0%
298 530
 
5.7%
326 636
 
6.9%
328 519
 
5.6%
333 618
 
6.7%
339 620
 
6.7%
346 780
 
8.4%
ValueCountFrequency (%)
364 955
10.3%
352 931
10.1%
346 780
8.4%
339 620
6.7%
333 618
6.7%
328 519
5.6%
326 636
6.9%
298 530
5.7%
295 558
6.0%
290 593
6.4%
Distinct78
Distinct (%)0.8%
Missing65
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean366.48939
Minimum0
Maximum3912
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size72.4 KiB
2023-04-22T13:08:07.230178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile260
Q1290
median328
Q3352
95-th percentile704
Maximum3912
Range3912
Interquartile range (IQR)62

Descriptive statistics

Standard deviation195.71844
Coefficient of variation (CV)0.53403574
Kurtosis56.237775
Mean366.48939
Median Absolute Deviation (MAD)36
Skewness5.8580884
Sum3368404
Variance38305.706
MonotonicityNot monotonic
2023-04-22T13:08:07.358642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
260 2095
22.6%
364 838
 
9.1%
352 825
 
8.9%
346 679
 
7.3%
326 553
 
6.0%
339 543
 
5.9%
333 532
 
5.7%
290 522
 
5.6%
295 494
 
5.3%
298 470
 
5.1%
Other values (68) 1640
17.7%
ValueCountFrequency (%)
0 2
 
< 0.1%
255 109
 
1.2%
260 2095
22.6%
290 522
 
5.6%
295 494
 
5.3%
298 470
 
5.1%
326 553
 
6.0%
328 460
 
5.0%
333 532
 
5.7%
339 543
 
5.9%
ValueCountFrequency (%)
3912 1
 
< 0.1%
3480 1
 
< 0.1%
3276 1
 
< 0.1%
3168 1
 
< 0.1%
2980 1
 
< 0.1%
2768 1
 
< 0.1%
2664 1
 
< 0.1%
2655 1
 
< 0.1%
2340 3
< 0.1%
2296 1
 
< 0.1%

FinalDescription
Categorical

Distinct5606
Distinct (%)60.6%
Missing5
Missing (%)0.1%
Memory size72.4 KiB
Redacted
1299 
SMALL ANIMAL RESCUE
 
168
BIRD TRAPPED IN CHIMNEY
 
92
CAT STUCK ON ROOF
 
65
BIRD TRAPPED IN NETTING
 
56
Other values (5601)
7571 

Length

Max length100
Median length89
Mean length32.556048
Min length8

Characters and Unicode

Total characters301176
Distinct characters55
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5069 ?
Unique (%)54.8%

Sample

1st rowRedacted
2nd rowRedacted
3rd rowRedacted
4th rowRedacted
5th rowRedacted

Common Values

ValueCountFrequency (%)
Redacted 1299
 
14.0%
SMALL ANIMAL RESCUE 168
 
1.8%
BIRD TRAPPED IN CHIMNEY 92
 
1.0%
CAT STUCK ON ROOF 65
 
0.7%
BIRD TRAPPED IN NETTING 56
 
0.6%
ASSIST RSPCA WITH CAT UP TREE 50
 
0.5%
CAT TRAPPED ON ROOF 49
 
0.5%
CAT STUCK UP TREE 40
 
0.4%
ASSIST RSPCA WITH CAT ON ROOF 37
 
0.4%
PIGEON TRAPPED IN NETTING 37
 
0.4%
Other values (5596) 7358
79.5%

Length

2023-04-22T13:08:07.497284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
in 4653
 
8.7%
trapped 4300
 
8.0%
cat 3318
 
6.2%
stuck 2077
 
3.9%
on 1547
 
2.9%
redacted 1299
 
2.4%
rspca 1283
 
2.4%
with 1254
 
2.3%
dog 1124
 
2.1%
904
 
1.7%
Other values (2451) 31948
59.5%

Most occurring characters

ValueCountFrequency (%)
47387
15.7%
E 26469
 
8.8%
T 22775
 
7.6%
A 21378
 
7.1%
N 19457
 
6.5%
R 19062
 
6.3%
I 18298
 
6.1%
S 13325
 
4.4%
P 13291
 
4.4%
O 13076
 
4.3%
Other values (45) 86658
28.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 243543
80.9%
Space Separator 47387
 
15.7%
Lowercase Letter 9126
 
3.0%
Dash Punctuation 904
 
0.3%
Other Punctuation 186
 
0.1%
Open Punctuation 15
 
< 0.1%
Close Punctuation 12
 
< 0.1%
Math Symbol 2
 
< 0.1%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 26469
10.9%
T 22775
 
9.4%
A 21378
 
8.8%
N 19457
 
8.0%
R 19062
 
7.8%
I 18298
 
7.5%
S 13325
 
5.5%
P 13291
 
5.5%
O 13076
 
5.4%
D 12992
 
5.3%
Other values (16) 63420
26.0%
Lowercase Letter
ValueCountFrequency (%)
e 2602
28.5%
d 2599
28.5%
t 1302
14.3%
c 1300
14.2%
a 1300
14.2%
i 5
 
0.1%
s 4
 
< 0.1%
r 3
 
< 0.1%
n 3
 
< 0.1%
p 2
 
< 0.1%
Other values (6) 6
 
0.1%
Other Punctuation
ValueCountFrequency (%)
* 48
25.8%
/ 45
24.2%
. 40
21.5%
, 35
18.8%
& 18
 
9.7%
Open Punctuation
ValueCountFrequency (%)
( 14
93.3%
[ 1
 
6.7%
Math Symbol
ValueCountFrequency (%)
+ 1
50.0%
> 1
50.0%
Space Separator
ValueCountFrequency (%)
47387
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 904
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 252669
83.9%
Common 48507
 
16.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 26469
 
10.5%
T 22775
 
9.0%
A 21378
 
8.5%
N 19457
 
7.7%
R 19062
 
7.5%
I 18298
 
7.2%
S 13325
 
5.3%
P 13291
 
5.3%
O 13076
 
5.2%
D 12992
 
5.1%
Other values (32) 72546
28.7%
Common
ValueCountFrequency (%)
47387
97.7%
- 904
 
1.9%
* 48
 
0.1%
/ 45
 
0.1%
. 40
 
0.1%
, 35
 
0.1%
& 18
 
< 0.1%
( 14
 
< 0.1%
) 12
 
< 0.1%
+ 1
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
47387
15.7%
E 26469
 
8.8%
T 22775
 
7.6%
A 21378
 
7.1%
N 19457
 
6.5%
R 19062
 
6.3%
I 18298
 
6.1%
S 13325
 
4.4%
P 13291
 
4.4%
O 13076
 
4.3%
Other values (45) 86658
28.8%
Distinct28
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size72.4 KiB
Cat
4537 
Bird
1877 
Dog
1405 
Fox
479 
Unknown - Domestic Animal Or Pet
 
220
Other values (23)
738 

Length

Max length55
Median length3
Mean length4.4103284
Min length3

Characters and Unicode

Total characters40822
Distinct characters42
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowDog
2nd rowFox
3rd rowDog
4th rowHorse
5th rowRabbit

Common Values

ValueCountFrequency (%)
Cat 4537
49.0%
Bird 1877
20.3%
Dog 1405
 
15.2%
Fox 479
 
5.2%
Unknown - Domestic Animal Or Pet 220
 
2.4%
Horse 210
 
2.3%
Deer 158
 
1.7%
Unknown - Wild Animal 104
 
1.1%
Squirrel 81
 
0.9%
Unknown - Heavy Livestock Animal 51
 
0.6%
Other values (18) 134
 
1.4%

Length

2023-04-22T13:08:07.614558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cat 4561
41.8%
bird 1877
17.2%
dog 1405
 
12.9%
fox 479
 
4.4%
383
 
3.5%
animal 383
 
3.5%
unknown 379
 
3.5%
or 220
 
2.0%
pet 220
 
2.0%
domestic 220
 
2.0%
Other values (27) 778
 
7.1%

Most occurring characters

ValueCountFrequency (%)
t 5105
12.5%
a 5070
12.4%
C 4546
11.1%
o 2774
 
6.8%
i 2746
 
6.7%
r 2685
 
6.6%
d 1993
 
4.9%
B 1881
 
4.6%
D 1783
 
4.4%
1649
 
4.0%
Other values (32) 10590
25.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 28309
69.3%
Uppercase Letter 10481
 
25.7%
Space Separator 1649
 
4.0%
Dash Punctuation 383
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 5105
18.0%
a 5070
17.9%
o 2774
9.8%
i 2746
9.7%
r 2685
9.5%
d 1993
 
7.0%
n 1545
 
5.5%
g 1423
 
5.0%
e 1251
 
4.4%
m 631
 
2.2%
Other values (15) 3086
10.9%
Uppercase Letter
ValueCountFrequency (%)
C 4546
43.4%
B 1881
17.9%
D 1783
 
17.0%
F 495
 
4.7%
U 379
 
3.6%
A 379
 
3.6%
H 284
 
2.7%
P 224
 
2.1%
O 220
 
2.1%
S 108
 
1.0%
Other values (5) 182
 
1.7%
Space Separator
ValueCountFrequency (%)
1649
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 383
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 38790
95.0%
Common 2032
 
5.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 5105
13.2%
a 5070
13.1%
C 4546
11.7%
o 2774
 
7.2%
i 2746
 
7.1%
r 2685
 
6.9%
d 1993
 
5.1%
B 1881
 
4.8%
D 1783
 
4.6%
n 1545
 
4.0%
Other values (30) 8662
22.3%
Common
ValueCountFrequency (%)
1649
81.2%
- 383
 
18.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40822
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 5105
12.5%
a 5070
12.4%
C 4546
11.1%
o 2774
 
6.8%
i 2746
 
6.7%
r 2685
 
6.6%
d 1993
 
4.9%
B 1881
 
4.6%
D 1783
 
4.4%
1649
 
4.0%
Other values (32) 10590
25.9%

OriginofCall
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size72.4 KiB
Person (mobile)
5623 
Person (land line)
3438 
Police
 
132
Other FRS
 
52
Person (running call)
 
5
Other values (3)
 
6

Length

Max length21
Median length15
Mean length15.951815
Min length6

Characters and Unicode

Total characters147650
Distinct characters29
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPerson (land line)
2nd rowPerson (land line)
3rd rowPerson (mobile)
4th rowPerson (mobile)
5th rowPerson (mobile)

Common Values

ValueCountFrequency (%)
Person (mobile) 5623
60.7%
Person (land line) 3438
37.1%
Police 132
 
1.4%
Other FRS 52
 
0.6%
Person (running call) 5
 
0.1%
Ambulance 2
 
< 0.1%
Coastguard 2
 
< 0.1%
Not known 2
 
< 0.1%

Length

2023-04-22T13:08:07.707628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-22T13:08:07.817631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
person 9066
41.6%
mobile 5623
25.8%
land 3438
 
15.8%
line 3438
 
15.8%
police 132
 
0.6%
other 52
 
0.2%
frs 52
 
0.2%
running 5
 
< 0.1%
call 5
 
< 0.1%
ambulance 2
 
< 0.1%
Other values (3) 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 18313
12.4%
n 15963
10.8%
o 14827
10.0%
l 12643
8.6%
12563
8.5%
P 9198
 
6.2%
i 9198
 
6.2%
r 9125
 
6.2%
s 9068
 
6.1%
( 9066
 
6.1%
Other values (19) 27686
18.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 107543
72.8%
Space Separator 12563
 
8.5%
Uppercase Letter 9412
 
6.4%
Open Punctuation 9066
 
6.1%
Close Punctuation 9066
 
6.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 18313
17.0%
n 15963
14.8%
o 14827
13.8%
l 12643
11.8%
i 9198
8.6%
r 9125
8.5%
s 9068
8.4%
m 5625
 
5.2%
b 5625
 
5.2%
a 3449
 
3.2%
Other values (8) 3707
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
P 9198
97.7%
O 52
 
0.6%
F 52
 
0.6%
R 52
 
0.6%
S 52
 
0.6%
A 2
 
< 0.1%
C 2
 
< 0.1%
N 2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
12563
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9066
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9066
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 116955
79.2%
Common 30695
 
20.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 18313
15.7%
n 15963
13.6%
o 14827
12.7%
l 12643
10.8%
P 9198
7.9%
i 9198
7.9%
r 9125
7.8%
s 9068
7.8%
m 5625
 
4.8%
b 5625
 
4.8%
Other values (16) 7370
6.3%
Common
ValueCountFrequency (%)
12563
40.9%
( 9066
29.5%
) 9066
29.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147650
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 18313
12.4%
n 15963
10.8%
o 14827
10.0%
l 12643
8.6%
12563
8.5%
P 9198
 
6.2%
i 9198
 
6.2%
r 9125
 
6.2%
s 9068
 
6.1%
( 9066
 
6.1%
Other values (19) 27686
18.8%

PropertyType
Categorical

Distinct185
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size72.4 KiB
House - single occupancy
2421 
Purpose Built Flats/Maisonettes - 4 to 9 storeys
817 
Purpose Built Flats/Maisonettes - Up to 3 storeys
774 
Tree scrub
 
409
Converted Flat/Maisonettes - 3 or more storeys
 
340
Other values (180)
4495 

Length

Max length78
Median length60
Mean length27.643366
Min length4

Characters and Unicode

Total characters255867
Distinct characters61
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)0.4%

Sample

1st rowHouse - single occupancy
2nd rowRailings
3rd rowPipe or drain
4th rowIntensive Farming Sheds (chickens, pigs etc)
5th rowHouse - single occupancy

Common Values

ValueCountFrequency (%)
House - single occupancy 2421
26.2%
Purpose Built Flats/Maisonettes - 4 to 9 storeys 817
 
8.8%
Purpose Built Flats/Maisonettes - Up to 3 storeys 774
 
8.4%
Tree scrub 409
 
4.4%
Converted Flat/Maisonettes - 3 or more storeys 340
 
3.7%
Animal harm outdoors 338
 
3.7%
Car 309
 
3.3%
Domestic garden (vegetation not equipment) 297
 
3.2%
Converted Flat/Maisonette - Up to 2 storeys 266
 
2.9%
Park 228
 
2.5%
Other values (175) 3057
33.0%

Length

2023-04-22T13:08:07.961716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4983
 
13.1%
single 2560
 
6.7%
occupancy 2449
 
6.4%
house 2442
 
6.4%
storeys 2355
 
6.2%
to 1866
 
4.9%
purpose 1799
 
4.7%
built 1799
 
4.7%
flats/maisonettes 1734
 
4.6%
3 1124
 
3.0%
Other values (288) 14921
39.2%

Most occurring characters

ValueCountFrequency (%)
36561
14.3%
e 23329
 
9.1%
s 20612
 
8.1%
o 20532
 
8.0%
t 18345
 
7.2%
a 13291
 
5.2%
n 12434
 
4.9%
r 12266
 
4.8%
u 10796
 
4.2%
i 10712
 
4.2%
Other values (51) 76989
30.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 188960
73.9%
Space Separator 36561
 
14.3%
Uppercase Letter 17227
 
6.7%
Dash Punctuation 5002
 
2.0%
Other Punctuation 3986
 
1.6%
Decimal Number 3315
 
1.3%
Close Punctuation 408
 
0.2%
Open Punctuation 408
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 23329
12.3%
s 20612
10.9%
o 20532
10.9%
t 18345
9.7%
a 13291
 
7.0%
n 12434
 
6.6%
r 12266
 
6.5%
u 10796
 
5.7%
i 10712
 
5.7%
c 9567
 
5.1%
Other values (15) 37076
19.6%
Uppercase Letter
ValueCountFrequency (%)
H 2577
15.0%
F 2534
14.7%
P 2388
13.9%
M 2381
13.8%
B 1941
11.3%
C 1169
6.8%
U 1062
6.2%
R 626
 
3.6%
T 479
 
2.8%
A 349
 
2.0%
Other values (12) 1721
10.0%
Decimal Number
ValueCountFrequency (%)
3 1124
33.9%
4 817
24.6%
9 817
24.6%
2 271
 
8.2%
0 143
 
4.3%
1 143
 
4.3%
Other Punctuation
ValueCountFrequency (%)
/ 3757
94.3%
, 224
 
5.6%
. 4
 
0.1%
& 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
36561
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5002
100.0%
Close Punctuation
ValueCountFrequency (%)
) 408
100.0%
Open Punctuation
ValueCountFrequency (%)
( 408
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 206187
80.6%
Common 49680
 
19.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 23329
11.3%
s 20612
 
10.0%
o 20532
 
10.0%
t 18345
 
8.9%
a 13291
 
6.4%
n 12434
 
6.0%
r 12266
 
5.9%
u 10796
 
5.2%
i 10712
 
5.2%
c 9567
 
4.6%
Other values (37) 54303
26.3%
Common
ValueCountFrequency (%)
36561
73.6%
- 5002
 
10.1%
/ 3757
 
7.6%
3 1124
 
2.3%
4 817
 
1.6%
9 817
 
1.6%
) 408
 
0.8%
( 408
 
0.8%
2 271
 
0.5%
, 224
 
0.5%
Other values (4) 291
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 255867
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
36561
14.3%
e 23329
 
9.1%
s 20612
 
8.1%
o 20532
 
8.0%
t 18345
 
7.2%
a 13291
 
5.2%
n 12434
 
4.9%
r 12266
 
4.8%
u 10796
 
4.2%
i 10712
 
4.2%
Other values (51) 76989
30.1%

PropertyCategory
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size72.4 KiB
Dwelling
4830 
Outdoor
2407 
Non Residential
940 
Outdoor Structure
681 
Road Vehicle
 
364
Other values (2)
 
34

Length

Max length17
Median length8
Mean length9.2977528
Min length4

Characters and Unicode

Total characters86060
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDwelling
2nd rowOutdoor Structure
3rd rowOutdoor Structure
4th rowNon Residential
5th rowDwelling

Common Values

ValueCountFrequency (%)
Dwelling 4830
52.2%
Outdoor 2407
26.0%
Non Residential 940
 
10.2%
Outdoor Structure 681
 
7.4%
Road Vehicle 364
 
3.9%
Other Residential 30
 
0.3%
Boat 4
 
< 0.1%

Length

2023-04-22T13:08:08.087371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-22T13:08:08.197513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
dwelling 4830
42.9%
outdoor 3088
27.4%
residential 970
 
8.6%
non 940
 
8.3%
structure 681
 
6.0%
road 364
 
3.2%
vehicle 364
 
3.2%
other 30
 
0.3%
boat 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
l 10994
12.8%
e 8209
 
9.5%
o 7484
 
8.7%
i 7134
 
8.3%
n 6740
 
7.8%
t 5454
 
6.3%
w 4830
 
5.6%
D 4830
 
5.6%
g 4830
 
5.6%
r 4480
 
5.2%
Other values (13) 21075
24.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 72774
84.6%
Uppercase Letter 11271
 
13.1%
Space Separator 2015
 
2.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 10994
15.1%
e 8209
11.3%
o 7484
10.3%
i 7134
9.8%
n 6740
9.3%
t 5454
7.5%
w 4830
6.6%
g 4830
6.6%
r 4480
6.2%
u 4450
6.1%
Other values (5) 8169
11.2%
Uppercase Letter
ValueCountFrequency (%)
D 4830
42.9%
O 3118
27.7%
R 1334
 
11.8%
N 940
 
8.3%
S 681
 
6.0%
V 364
 
3.2%
B 4
 
< 0.1%
Space Separator
ValueCountFrequency (%)
2015
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 84045
97.7%
Common 2015
 
2.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 10994
13.1%
e 8209
9.8%
o 7484
 
8.9%
i 7134
 
8.5%
n 6740
 
8.0%
t 5454
 
6.5%
w 4830
 
5.7%
D 4830
 
5.7%
g 4830
 
5.7%
r 4480
 
5.3%
Other values (12) 19060
22.7%
Common
ValueCountFrequency (%)
2015
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 86060
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 10994
12.8%
e 8209
 
9.5%
o 7484
 
8.7%
i 7134
 
8.3%
n 6740
 
7.8%
t 5454
 
6.3%
w 4830
 
5.6%
D 4830
 
5.6%
g 4830
 
5.6%
r 4480
 
5.2%
Other values (13) 21075
24.5%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size72.4 KiB
Other animal assistance
4432 
Animal rescue from height
3453 
Animal rescue from below ground
914 
Animal rescue from water
457 

Length

Max length31
Median length25
Mean length24.585458
Min length23

Characters and Unicode

Total characters227563
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOther animal assistance
2nd rowOther animal assistance
3rd rowAnimal rescue from below ground
4th rowAnimal rescue from water
5th rowOther animal assistance

Common Values

ValueCountFrequency (%)
Other animal assistance 4432
47.9%
Animal rescue from height 3453
37.3%
Animal rescue from below ground 914
 
9.9%
Animal rescue from water 457
 
4.9%

Length

2023-04-22T13:08:08.314388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-22T13:08:08.418463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
animal 9256
27.6%
rescue 4824
14.4%
from 4824
14.4%
other 4432
13.2%
assistance 4432
13.2%
height 3453
 
10.3%
below 914
 
2.7%
ground 914
 
2.7%
water 457
 
1.4%

Most occurring characters

ValueCountFrequency (%)
24250
10.7%
e 23336
10.3%
a 23009
10.1%
s 18120
 
8.0%
i 17141
 
7.5%
r 15451
 
6.8%
n 14602
 
6.4%
m 14080
 
6.2%
t 12774
 
5.6%
h 11338
 
5.0%
Other values (11) 53462
23.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 194057
85.3%
Space Separator 24250
 
10.7%
Uppercase Letter 9256
 
4.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 23336
12.0%
a 23009
11.9%
s 18120
9.3%
i 17141
8.8%
r 15451
8.0%
n 14602
7.5%
m 14080
7.3%
t 12774
 
6.6%
h 11338
 
5.8%
l 10170
 
5.2%
Other values (8) 34036
17.5%
Uppercase Letter
ValueCountFrequency (%)
A 4824
52.1%
O 4432
47.9%
Space Separator
ValueCountFrequency (%)
24250
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 203313
89.3%
Common 24250
 
10.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 23336
11.5%
a 23009
11.3%
s 18120
8.9%
i 17141
 
8.4%
r 15451
 
7.6%
n 14602
 
7.2%
m 14080
 
6.9%
t 12774
 
6.3%
h 11338
 
5.6%
l 10170
 
5.0%
Other values (10) 43292
21.3%
Common
ValueCountFrequency (%)
24250
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 227563
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
24250
10.7%
e 23336
10.3%
a 23009
10.1%
s 18120
 
8.0%
i 17141
 
7.5%
r 15451
 
6.8%
n 14602
 
6.4%
m 14080
 
6.2%
t 12774
 
5.6%
h 11338
 
5.0%
Other values (11) 53462
23.5%
Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size72.4 KiB
Animal rescue from height - Domestic pet
2169 
Assist trapped domestic animal
1924 
Animal rescue from height - Bird
1169 
Assist trapped wild animal
823 
Animal rescue from below ground - Domestic pet
720 
Other values (19)
2451 

Length

Max length58
Median length52
Mean length37.967156
Min length26

Characters and Unicode

Total characters351424
Distinct characters31
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAnimal assistance involving livestock - Other action
2nd rowAnimal assistance involving livestock - Other action
3rd rowAnimal rescue from below ground - Domestic pet
4th rowAnimal rescue from water - Farm animal
5th rowAnimal assistance involving livestock - Other action

Common Values

ValueCountFrequency (%)
Animal rescue from height - Domestic pet 2169
23.4%
Assist trapped domestic animal 1924
20.8%
Animal rescue from height - Bird 1169
12.6%
Assist trapped wild animal 823
 
8.9%
Animal rescue from below ground - Domestic pet 720
 
7.8%
Animal assistance involving livestock - Other action 641
 
6.9%
Animal assistance involving domestic animal - Other action 556
 
6.0%
Animal rescue from water - Domestic pet 266
 
2.9%
Animal assistance involving wild animal - Other action 186
 
2.0%
Wild animal rescue from height 111
 
1.2%
Other values (14) 691
 
7.5%

Length

2023-04-22T13:08:08.517421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
animal 10323
18.4%
6032
10.8%
domestic 5765
10.3%
from 4824
8.6%
rescue 4824
8.6%
height 3453
 
6.2%
pet 3155
 
5.6%
assist 2807
 
5.0%
trapped 2807
 
5.0%
involving 1535
 
2.7%
Other values (15) 10467
18.7%

Most occurring characters

ValueCountFrequency (%)
46796
13.3%
i 31767
 
9.0%
e 29914
 
8.5%
s 24198
 
6.9%
t 23542
 
6.7%
a 22385
 
6.4%
m 21212
 
6.0%
n 17163
 
4.9%
r 16825
 
4.8%
o 16164
 
4.6%
Other values (21) 101458
28.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 283308
80.6%
Space Separator 46796
 
13.3%
Uppercase Letter 15288
 
4.4%
Dash Punctuation 6032
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 31767
11.2%
e 29914
10.6%
s 24198
 
8.5%
t 23542
 
8.3%
a 22385
 
7.9%
m 21212
 
7.5%
n 17163
 
6.1%
r 16825
 
5.9%
o 16164
 
5.7%
l 14859
 
5.2%
Other values (12) 65279
23.0%
Uppercase Letter
ValueCountFrequency (%)
A 8991
58.8%
D 3155
 
20.6%
O 1383
 
9.0%
B 1316
 
8.6%
W 265
 
1.7%
L 90
 
0.6%
F 88
 
0.6%
Space Separator
ValueCountFrequency (%)
46796
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 298596
85.0%
Common 52828
 
15.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 31767
 
10.6%
e 29914
 
10.0%
s 24198
 
8.1%
t 23542
 
7.9%
a 22385
 
7.5%
m 21212
 
7.1%
n 17163
 
5.7%
r 16825
 
5.6%
o 16164
 
5.4%
l 14859
 
5.0%
Other values (19) 80567
27.0%
Common
ValueCountFrequency (%)
46796
88.6%
- 6032
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 351424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
46796
13.3%
i 31767
 
9.0%
e 29914
 
8.5%
s 24198
 
6.9%
t 23542
 
6.7%
a 22385
 
6.4%
m 21212
 
6.0%
n 17163
 
4.9%
r 16825
 
4.8%
o 16164
 
4.6%
Other values (21) 101458
28.9%

WardCode
Categorical

Distinct758
Distinct (%)8.2%
Missing10
Missing (%)0.1%
Memory size72.4 KiB
E05013971
 
46
E05009327
 
45
E05013703
 
41
E05013689
 
37
E05013808
 
36
Other values (753)
9041 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters83214
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)0.5%

Sample

1st rowE05011467
2nd rowE05000169
3rd rowE05013756
4th rowE05013568
5th rowE05013971

Common Values

ValueCountFrequency (%)
E05013971 46
 
0.5%
E05009327 45
 
0.5%
E05013703 41
 
0.4%
E05013689 37
 
0.4%
E05013808 36
 
0.4%
E05014010 36
 
0.4%
E05013570 35
 
0.4%
E05014081 34
 
0.4%
E05013938 32
 
0.3%
E05013695 32
 
0.3%
Other values (748) 8872
95.9%

Length

2023-04-22T13:08:08.617675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
e05013971 46
 
0.5%
e05009327 45
 
0.5%
e05013703 41
 
0.4%
e05013689 37
 
0.4%
e05013808 36
 
0.4%
e05014010 36
 
0.4%
e05013570 35
 
0.4%
e05014081 34
 
0.4%
e05013938 32
 
0.3%
e05013695 32
 
0.3%
Other values (748) 8872
96.0%

Most occurring characters

ValueCountFrequency (%)
0 23058
27.7%
5 12035
14.5%
1 12003
14.4%
E 9246
11.1%
3 8193
 
9.8%
9 4071
 
4.9%
4 3247
 
3.9%
7 3241
 
3.9%
6 3113
 
3.7%
8 2648
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 73968
88.9%
Uppercase Letter 9246
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23058
31.2%
5 12035
16.3%
1 12003
16.2%
3 8193
 
11.1%
9 4071
 
5.5%
4 3247
 
4.4%
7 3241
 
4.4%
6 3113
 
4.2%
8 2648
 
3.6%
2 2359
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
E 9246
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 73968
88.9%
Latin 9246
 
11.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23058
31.2%
5 12035
16.3%
1 12003
16.2%
3 8193
 
11.1%
9 4071
 
5.5%
4 3247
 
4.4%
7 3241
 
4.4%
6 3113
 
4.2%
8 2648
 
3.6%
2 2359
 
3.2%
Latin
ValueCountFrequency (%)
E 9246
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 83214
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23058
27.7%
5 12035
14.5%
1 12003
14.4%
E 9246
11.1%
3 8193
 
9.8%
9 4071
 
4.9%
4 3247
 
3.9%
7 3241
 
3.9%
6 3113
 
3.7%
8 2648
 
3.2%

Ward
Categorical

Distinct1182
Distinct (%)12.8%
Missing10
Missing (%)0.1%
Memory size72.4 KiB
Gooshays
 
42
Mile End
 
38
West End
 
35
Heathrow Villages
 
35
Battersea Park
 
35
Other values (1177)
9061 

Length

Max length36
Median length31
Mean length12.809864
Min length3

Characters and Unicode

Total characters118440
Distinct characters57
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique210 ?
Unique (%)2.3%

Sample

1st rowCrystal Palace & Upper Norwood
2nd rowWoodside
3rd rowCarshalton Central
4th rowHarefield Village
5th rowGooshays

Common Values

ValueCountFrequency (%)
Gooshays 42
 
0.5%
Mile End 38
 
0.4%
West End 35
 
0.4%
Heathrow Villages 35
 
0.4%
Battersea Park 35
 
0.4%
Finsbury Park 35
 
0.4%
Village 33
 
0.4%
Kingston Town 32
 
0.3%
Ponders End 32
 
0.3%
Regent's Park 32
 
0.3%
Other values (1172) 8897
96.1%

Length

2023-04-22T13:08:08.852160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1183
 
6.5%
park 672
 
3.7%
south 458
 
2.5%
west 451
 
2.5%
green 442
 
2.4%
st 430
 
2.4%
hill 415
 
2.3%
east 330
 
1.8%
town 327
 
1.8%
north 306
 
1.7%
Other values (657) 13142
72.4%

Most occurring characters

ValueCountFrequency (%)
e 9105
 
7.7%
8910
 
7.5%
o 6947
 
5.9%
a 6736
 
5.7%
r 6489
 
5.5%
n 6309
 
5.3%
t 5838
 
4.9%
l 5468
 
4.6%
s 4402
 
3.7%
i 4011
 
3.4%
Other values (47) 54225
45.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 76761
64.8%
Uppercase Letter 30603
 
25.8%
Space Separator 8910
 
7.5%
Other Punctuation 2138
 
1.8%
Dash Punctuation 28
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 2535
 
8.3%
S 2531
 
8.3%
H 2494
 
8.1%
C 1943
 
6.3%
N 1797
 
5.9%
R 1692
 
5.5%
W 1649
 
5.4%
L 1648
 
5.4%
O 1623
 
5.3%
T 1613
 
5.3%
Other values (16) 11078
36.2%
Lowercase Letter
ValueCountFrequency (%)
e 9105
11.9%
o 6947
 
9.1%
a 6736
 
8.8%
r 6489
 
8.5%
n 6309
 
8.2%
t 5838
 
7.6%
l 5468
 
7.1%
s 4402
 
5.7%
i 4011
 
5.2%
h 3617
 
4.7%
Other values (15) 17839
23.2%
Other Punctuation
ValueCountFrequency (%)
& 1183
55.3%
' 480
22.5%
. 430
 
20.1%
, 45
 
2.1%
Space Separator
ValueCountFrequency (%)
8910
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 107364
90.6%
Common 11076
 
9.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 9105
 
8.5%
o 6947
 
6.5%
a 6736
 
6.3%
r 6489
 
6.0%
n 6309
 
5.9%
t 5838
 
5.4%
l 5468
 
5.1%
s 4402
 
4.1%
i 4011
 
3.7%
h 3617
 
3.4%
Other values (41) 48442
45.1%
Common
ValueCountFrequency (%)
8910
80.4%
& 1183
 
10.7%
' 480
 
4.3%
. 430
 
3.9%
, 45
 
0.4%
- 28
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 118440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 9105
 
7.7%
8910
 
7.5%
o 6947
 
5.9%
a 6736
 
5.7%
r 6489
 
5.5%
n 6309
 
5.3%
t 5838
 
4.9%
l 5468
 
4.6%
s 4402
 
3.7%
i 4011
 
3.4%
Other values (47) 54225
45.8%

BoroughCode
Categorical

Distinct37
Distinct (%)0.4%
Missing12
Missing (%)0.1%
Memory size72.4 KiB
E09000010
 
422
E09000008
 
386
E09000028
 
374
E09000003
 
368
E09000030
 
352
Other values (32)
7342 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters83196
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowE09000008
2nd rowE09000008
3rd rowE09000029
4th rowE09000017
5th rowE09000016

Common Values

ValueCountFrequency (%)
E09000010 422
 
4.6%
E09000008 386
 
4.2%
E09000028 374
 
4.0%
E09000003 368
 
4.0%
E09000030 352
 
3.8%
E09000014 340
 
3.7%
E09000025 332
 
3.6%
E09000022 330
 
3.6%
E09000031 324
 
3.5%
E09000006 319
 
3.4%
Other values (27) 5697
61.5%

Length

2023-04-22T13:08:08.957227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
e09000010 422
 
4.6%
e09000008 386
 
4.2%
e09000028 374
 
4.0%
e09000003 368
 
4.0%
e09000030 352
 
3.8%
e09000014 340
 
3.7%
e09000025 332
 
3.6%
e09000022 330
 
3.6%
e09000031 324
 
3.5%
e09000006 319
 
3.5%
Other values (27) 5697
61.6%

Most occurring characters

ValueCountFrequency (%)
0 49640
59.7%
9 10042
 
12.1%
E 9244
 
11.1%
2 3796
 
4.6%
1 3749
 
4.5%
3 2403
 
2.9%
8 1033
 
1.2%
6 905
 
1.1%
7 850
 
1.0%
5 775
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 73952
88.9%
Uppercase Letter 9244
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 49640
67.1%
9 10042
 
13.6%
2 3796
 
5.1%
1 3749
 
5.1%
3 2403
 
3.2%
8 1033
 
1.4%
6 905
 
1.2%
7 850
 
1.1%
5 775
 
1.0%
4 759
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
E 9244
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 73952
88.9%
Latin 9244
 
11.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 49640
67.1%
9 10042
 
13.6%
2 3796
 
5.1%
1 3749
 
5.1%
3 2403
 
3.2%
8 1033
 
1.4%
6 905
 
1.2%
7 850
 
1.1%
5 775
 
1.0%
4 759
 
1.0%
Latin
ValueCountFrequency (%)
E 9244
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 83196
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 49640
59.7%
9 10042
 
12.1%
E 9244
 
11.1%
2 3796
 
4.6%
1 3749
 
4.5%
3 2403
 
2.9%
8 1033
 
1.2%
6 905
 
1.1%
7 850
 
1.0%
5 775
 
0.9%

Borough
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct70
Distinct (%)0.8%
Missing12
Missing (%)0.1%
Memory size72.4 KiB
ENFIELD
 
258
BARNET
 
217
CROYDON
 
216
SOUTHWARK
 
214
HARINGEY
 
203
Other values (65)
8136 

Length

Max length22
Median length14
Mean length9.5719386
Min length5

Characters and Unicode

Total characters88483
Distinct characters47
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowCroydon
2nd rowCroydon
3rd rowSutton
4th rowHillingdon
5th rowHavering

Common Values

ValueCountFrequency (%)
ENFIELD 258
 
2.8%
BARNET 217
 
2.3%
CROYDON 216
 
2.3%
SOUTHWARK 214
 
2.3%
HARINGEY 203
 
2.2%
TOWER HAMLETS 196
 
2.1%
NEWHAM 195
 
2.1%
EALING 191
 
2.1%
GREENWICH 184
 
2.0%
REDBRIDGE 184
 
2.0%
Other values (60) 7186
77.6%

Length

2023-04-22T13:08:09.052768image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and 676
 
5.6%
upon 428
 
3.5%
thames 428
 
3.5%
enfield 422
 
3.5%
croydon 386
 
3.2%
southwark 374
 
3.1%
barnet 368
 
3.0%
tower 352
 
2.9%
hamlets 352
 
2.9%
haringey 340
 
2.8%
Other values (37) 8031
66.1%

Most occurring characters

ValueCountFrequency (%)
E 5693
 
6.4%
N 5246
 
5.9%
H 4429
 
5.0%
A 4025
 
4.5%
n 3631
 
4.1%
e 3584
 
4.1%
R 3552
 
4.0%
T 3426
 
3.9%
a 3131
 
3.5%
O 2978
 
3.4%
Other values (37) 48788
55.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 53712
60.7%
Lowercase Letter 31858
36.0%
Space Separator 2913
 
3.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 5693
 
10.6%
N 5246
 
9.8%
H 4429
 
8.2%
A 4025
 
7.5%
R 3552
 
6.6%
T 3426
 
6.4%
O 2978
 
5.5%
I 2941
 
5.5%
L 2699
 
5.0%
S 2673
 
5.0%
Other values (13) 16050
29.9%
Lowercase Letter
ValueCountFrequency (%)
n 3631
11.4%
e 3584
11.2%
a 3131
 
9.8%
o 2267
 
7.1%
r 2181
 
6.8%
t 2027
 
6.4%
i 1913
 
6.0%
m 1882
 
5.9%
h 1663
 
5.2%
s 1542
 
4.8%
Other values (13) 8037
25.2%
Space Separator
ValueCountFrequency (%)
2913
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 85570
96.7%
Common 2913
 
3.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 5693
 
6.7%
N 5246
 
6.1%
H 4429
 
5.2%
A 4025
 
4.7%
n 3631
 
4.2%
e 3584
 
4.2%
R 3552
 
4.2%
T 3426
 
4.0%
a 3131
 
3.7%
O 2978
 
3.5%
Other values (36) 45875
53.6%
Common
ValueCountFrequency (%)
2913
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88483
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 5693
 
6.4%
N 5246
 
5.9%
H 4429
 
5.0%
A 4025
 
4.5%
n 3631
 
4.1%
e 3584
 
4.1%
R 3552
 
4.0%
T 3426
 
3.9%
a 3131
 
3.5%
O 2978
 
3.4%
Other values (37) 48788
55.1%

StnGroundName
Categorical

Distinct108
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size72.4 KiB
Enfield
 
178
Ilford
 
167
Tottenham
 
161
Dagenham
 
154
Edmonton
 
154
Other values (103)
8442 

Length

Max length16
Median length14
Mean length8.4965428
Min length4

Characters and Unicode

Total characters78644
Distinct characters42
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowNorbury
2nd rowWoodside
3rd rowWallington
4th rowRuislip
5th rowHarold Hill

Common Values

ValueCountFrequency (%)
Enfield 178
 
1.9%
Ilford 167
 
1.8%
Tottenham 161
 
1.7%
Dagenham 154
 
1.7%
Edmonton 154
 
1.7%
Hornsey 151
 
1.6%
West Hampstead 149
 
1.6%
North Kensington 146
 
1.6%
Holloway 144
 
1.6%
Bethnal Green 135
 
1.5%
Other values (98) 7717
83.4%

Length

2023-04-22T13:08:09.165251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hill 273
 
2.5%
west 255
 
2.4%
kensington 207
 
1.9%
green 192
 
1.8%
enfield 178
 
1.6%
ilford 167
 
1.5%
tottenham 161
 
1.5%
east 158
 
1.5%
edmonton 154
 
1.4%
dagenham 154
 
1.4%
Other values (106) 8948
82.5%

Most occurring characters

ValueCountFrequency (%)
o 7229
 
9.2%
n 6580
 
8.4%
e 6262
 
8.0%
t 6190
 
7.9%
a 5114
 
6.5%
l 4639
 
5.9%
i 4098
 
5.2%
r 3824
 
4.9%
h 3588
 
4.6%
d 3055
 
3.9%
Other values (32) 28065
35.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 66206
84.2%
Uppercase Letter 10847
 
13.8%
Space Separator 1591
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 7229
10.9%
n 6580
9.9%
e 6262
9.5%
t 6190
 
9.3%
a 5114
 
7.7%
l 4639
 
7.0%
i 4098
 
6.2%
r 3824
 
5.8%
h 3588
 
5.4%
d 3055
 
4.6%
Other values (12) 15627
23.6%
Uppercase Letter
ValueCountFrequency (%)
H 1728
15.9%
S 1037
9.6%
W 992
 
9.1%
E 887
 
8.2%
B 793
 
7.3%
P 672
 
6.2%
N 670
 
6.2%
T 516
 
4.8%
C 494
 
4.6%
K 492
 
4.5%
Other values (9) 2566
23.7%
Space Separator
ValueCountFrequency (%)
1591
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 77053
98.0%
Common 1591
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 7229
 
9.4%
n 6580
 
8.5%
e 6262
 
8.1%
t 6190
 
8.0%
a 5114
 
6.6%
l 4639
 
6.0%
i 4098
 
5.3%
r 3824
 
5.0%
h 3588
 
4.7%
d 3055
 
4.0%
Other values (31) 26474
34.4%
Common
ValueCountFrequency (%)
1591
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78644
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 7229
 
9.2%
n 6580
 
8.4%
e 6262
 
8.0%
t 6190
 
7.9%
a 5114
 
6.5%
l 4639
 
5.9%
i 4098
 
5.2%
r 3824
 
4.9%
h 3588
 
4.6%
d 3055
 
3.9%
Other values (32) 28065
35.7%

UPRN
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3262
Distinct (%)95.7%
Missing5849
Missing (%)63.2%
Infinite0
Infinite (%)0.0%
Mean5.1128292 × 1010
Minimum5005910
Maximum2.0000444 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size72.4 KiB
2023-04-22T13:08:09.279299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5005910
5-th percentile6145467.2
Q12.0212793 × 108
median1.0033527 × 1010
Q31.0002192 × 1011
95-th percentile2.0000121 × 1011
Maximum2.0000444 × 1011
Range1.9999943 × 1011
Interquartile range (IQR)9.9819795 × 1010

Descriptive statistics

Standard deviation5.9863106 × 1010
Coefficient of variation (CV)1.1708411
Kurtosis-0.12363727
Mean5.1128292 × 1010
Median Absolute Deviation (MAD)1.0021457 × 1010
Skewness0.9053048
Sum1.7419409 × 1014
Variance3.5835914 × 1021
MonotonicityNot monotonic
2023-04-22T13:08:09.407282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.000213456 × 10116
 
0.1%
207178584 5
 
0.1%
121033252 4
 
< 0.1%
48119014 4
 
< 0.1%
202099001 4
 
< 0.1%
2.0000121 × 10114
 
< 0.1%
2.000010838 × 10114
 
< 0.1%
1.000229024 × 10113
 
< 0.1%
1.00021389 × 10113
 
< 0.1%
5870070369 3
 
< 0.1%
Other values (3252) 3367
36.4%
(Missing) 5849
63.2%
ValueCountFrequency (%)
5005910 1
< 0.1%
5011266 1
< 0.1%
5012018 1
< 0.1%
5014827 1
< 0.1%
5017447 1
< 0.1%
5018198 1
< 0.1%
5019405 1
< 0.1%
5019782 1
< 0.1%
5021535 1
< 0.1%
5022289 1
< 0.1%
ValueCountFrequency (%)
2.000044388 × 10111
< 0.1%
2.000044378 × 10111
< 0.1%
2.000044378 × 10111
< 0.1%
2.000044348 × 10111
< 0.1%
2.000040083 × 10111
< 0.1%
2.000039955 × 10111
< 0.1%
2.000039909 × 10111
< 0.1%
2.000039865 × 10111
< 0.1%
2.00003973 × 10111
< 0.1%
2.000039726 × 10111
< 0.1%

Street
Categorical

Distinct6894
Distinct (%)74.5%
Missing0
Missing (%)0.0%
Memory size72.4 KiB
HIGH STREET
 
43
High Street
 
24
HIGH ROAD
 
23
LONDON ROAD
 
22
THE BROADWAY
 
19
Other values (6889)
9125 

Length

Max length94
Median length72
Mean length13.667135
Min length4

Characters and Unicode

Total characters126503
Distinct characters69
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5509 ?
Unique (%)59.5%

Sample

1st rowWaddington Way
2nd rowGrasmere Road
3rd rowMill Lane
4th rowPark Lane
5th rowSwindon Lane

Common Values

ValueCountFrequency (%)
HIGH STREET 43
 
0.5%
High Street 24
 
0.3%
HIGH ROAD 23
 
0.2%
LONDON ROAD 22
 
0.2%
THE BROADWAY 19
 
0.2%
High Road 16
 
0.2%
CHURCH STREET 15
 
0.2%
STATION ROAD 13
 
0.1%
London Road 13
 
0.1%
Park Road 12
 
0.1%
Other values (6884) 9056
97.8%

Length

2023-04-22T13:08:09.556309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
road 4055
 
20.3%
street 870
 
4.4%
avenue 673
 
3.4%
lane 575
 
2.9%
close 514
 
2.6%
park 353
 
1.8%
gardens 349
 
1.7%
way 308
 
1.5%
drive 236
 
1.2%
hill 227
 
1.1%
Other values (4479) 11839
59.2%

Most occurring characters

ValueCountFrequency (%)
10743
 
8.5%
R 9295
 
7.3%
E 8215
 
6.5%
A 7805
 
6.2%
O 6200
 
4.9%
e 5590
 
4.4%
a 4853
 
3.8%
D 4746
 
3.8%
S 4380
 
3.5%
L 4375
 
3.5%
Other values (59) 60301
47.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 75375
59.6%
Lowercase Letter 40199
31.8%
Space Separator 10743
 
8.5%
Other Punctuation 124
 
0.1%
Decimal Number 53
 
< 0.1%
Dash Punctuation 7
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 9295
12.3%
E 8215
10.9%
A 7805
10.4%
O 6200
 
8.2%
D 4746
 
6.3%
S 4380
 
5.8%
L 4375
 
5.8%
N 4258
 
5.6%
T 3957
 
5.2%
C 2868
 
3.8%
Other values (16) 19276
25.6%
Lowercase Letter
ValueCountFrequency (%)
e 5590
13.9%
a 4853
12.1%
o 4324
10.8%
r 3459
8.6%
d 3050
7.6%
n 2899
 
7.2%
t 2522
 
6.3%
l 2382
 
5.9%
s 1830
 
4.6%
i 1685
 
4.2%
Other values (16) 7605
18.9%
Decimal Number
ValueCountFrequency (%)
1 11
20.8%
2 9
17.0%
3 8
15.1%
0 8
15.1%
8 4
 
7.5%
7 4
 
7.5%
6 3
 
5.7%
5 3
 
5.7%
4 2
 
3.8%
9 1
 
1.9%
Other Punctuation
ValueCountFrequency (%)
' 66
53.2%
. 55
44.4%
/ 3
 
2.4%
Space Separator
ValueCountFrequency (%)
10743
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 115574
91.4%
Common 10929
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 9295
 
8.0%
E 8215
 
7.1%
A 7805
 
6.8%
O 6200
 
5.4%
e 5590
 
4.8%
a 4853
 
4.2%
D 4746
 
4.1%
S 4380
 
3.8%
L 4375
 
3.8%
o 4324
 
3.7%
Other values (42) 55791
48.3%
Common
ValueCountFrequency (%)
10743
98.3%
' 66
 
0.6%
. 55
 
0.5%
1 11
 
0.1%
2 9
 
0.1%
3 8
 
0.1%
0 8
 
0.1%
- 7
 
0.1%
8 4
 
< 0.1%
7 4
 
< 0.1%
Other values (7) 14
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 126503
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10743
 
8.5%
R 9295
 
7.3%
E 8215
 
6.5%
A 7805
 
6.2%
O 6200
 
4.9%
e 5590
 
4.4%
a 4853
 
3.8%
D 4746
 
3.8%
S 4380
 
3.5%
L 4375
 
3.5%
Other values (59) 60301
47.7%

USRN
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6198
Distinct (%)76.5%
Missing1156
Missing (%)12.5%
Infinite0
Infinite (%)0.0%
Mean20967182
Minimum4802548
Maximum40500307
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size72.4 KiB
2023-04-22T13:08:09.679590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4802548
5-th percentile19900629
Q120502322
median21300846
Q322200954
95-th percentile22847381
Maximum40500307
Range35697759
Interquartile range (IQR)1698631.5

Descriptive statistics

Standard deviation2520415.7
Coefficient of variation (CV)0.12020765
Kurtosis18.786814
Mean20967182
Median Absolute Deviation (MAD)800408
Skewness-3.8847218
Sum1.6983418 × 1011
Variance6.3524955 × 1012
MonotonicityNot monotonic
2023-04-22T13:08:09.809096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22306023 20
 
0.2%
22837350 12
 
0.1%
21300661 11
 
0.1%
22004177 9
 
0.1%
20201631 8
 
0.1%
20702448 8
 
0.1%
22844950 8
 
0.1%
19900172 8
 
0.1%
8400794 8
 
0.1%
20703459 8
 
0.1%
Other values (6188) 8000
86.4%
(Missing) 1156
 
12.5%
ValueCountFrequency (%)
4802548 1
< 0.1%
4810028 2
< 0.1%
8100080 1
< 0.1%
8100101 1
< 0.1%
8100118 2
< 0.1%
8100124 1
< 0.1%
8100150 1
< 0.1%
8100228 1
< 0.1%
8100241 1
< 0.1%
8100251 1
< 0.1%
ValueCountFrequency (%)
40500307 1
< 0.1%
39500869 1
< 0.1%
35200389 1
< 0.1%
34900219 1
< 0.1%
32102382 1
< 0.1%
22906955 1
< 0.1%
22906927 1
< 0.1%
22906902 1
< 0.1%
22906892 1
< 0.1%
22906841 1
< 0.1%

PostcodeDistrict
Categorical

Distinct276
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size72.4 KiB
CR0
 
159
E17
 
128
E14
 
121
N1
 
113
SE1
 
102
Other values (271)
8633 

Length

Max length4
Median length3
Mean length3.0761668
Min length2

Characters and Unicode

Total characters28473
Distinct characters33
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)0.2%

Sample

1st rowSE19
2nd rowSE25
3rd rowSM5
4th rowUB9
5th rowRM3

Common Values

ValueCountFrequency (%)
CR0 159
 
1.7%
E17 128
 
1.4%
E14 121
 
1.3%
N1 113
 
1.2%
SE1 102
 
1.1%
NW1 100
 
1.1%
E4 100
 
1.1%
NW10 99
 
1.1%
N17 96
 
1.0%
SE9 90
 
1.0%
Other values (266) 8148
88.0%

Length

2023-04-22T13:08:09.930344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cr0 159
 
1.7%
e17 128
 
1.4%
e14 121
 
1.3%
n1 113
 
1.2%
se1 102
 
1.1%
nw1 100
 
1.1%
e4 100
 
1.1%
nw10 99
 
1.1%
n17 96
 
1.0%
se9 90
 
1.0%
Other values (266) 8148
88.0%

Most occurring characters

ValueCountFrequency (%)
1 4435
15.6%
E 2760
 
9.7%
W 2727
 
9.6%
S 2293
 
8.1%
N 2002
 
7.0%
2 1307
 
4.6%
R 1123
 
3.9%
3 995
 
3.5%
6 969
 
3.4%
4 911
 
3.2%
Other values (23) 8951
31.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 15866
55.7%
Decimal Number 12607
44.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 2760
17.4%
W 2727
17.2%
S 2293
14.5%
N 2002
12.6%
R 1123
7.1%
T 652
 
4.1%
M 637
 
4.0%
B 624
 
3.9%
A 611
 
3.9%
C 500
 
3.2%
Other values (13) 1937
12.2%
Decimal Number
ValueCountFrequency (%)
1 4435
35.2%
2 1307
 
10.4%
3 995
 
7.9%
6 969
 
7.7%
4 911
 
7.2%
7 894
 
7.1%
5 893
 
7.1%
8 828
 
6.6%
0 734
 
5.8%
9 641
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 15866
55.7%
Common 12607
44.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 2760
17.4%
W 2727
17.2%
S 2293
14.5%
N 2002
12.6%
R 1123
7.1%
T 652
 
4.1%
M 637
 
4.0%
B 624
 
3.9%
A 611
 
3.9%
C 500
 
3.2%
Other values (13) 1937
12.2%
Common
ValueCountFrequency (%)
1 4435
35.2%
2 1307
 
10.4%
3 995
 
7.9%
6 969
 
7.7%
4 911
 
7.2%
7 894
 
7.1%
5 893
 
7.1%
8 828
 
6.6%
0 734
 
5.8%
9 641
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28473
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4435
15.6%
E 2760
 
9.7%
W 2727
 
9.6%
S 2293
 
8.1%
N 2002
 
7.0%
2 1307
 
4.6%
R 1123
 
3.9%
3 995
 
3.5%
6 969
 
3.4%
4 911
 
3.2%
Other values (23) 8951
31.4%

Easting_m
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4083
Distinct (%)92.3%
Missing4830
Missing (%)52.2%
Infinite0
Infinite (%)0.0%
Mean531134.84
Minimum500025
Maximum571394
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size72.4 KiB
2023-04-22T13:08:10.037667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum500025
5-th percentile511819.5
Q1523950
median531793
Q3537984
95-th percentile550158
Maximum571394
Range71369
Interquartile range (IQR)14034

Descriptive statistics

Standard deviation11167.807
Coefficient of variation (CV)0.021026313
Kurtosis-0.30942252
Mean531134.84
Median Absolute Deviation (MAD)7116.5
Skewness-0.078147601
Sum2.3508028 × 109
Variance1.2471992 × 108
MonotonicityNot monotonic
2023-04-22T13:08:10.174551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
536288 5
 
0.1%
528250 4
 
< 0.1%
537359 4
 
< 0.1%
530695 4
 
< 0.1%
528483 4
 
< 0.1%
533420 3
 
< 0.1%
534951 3
 
< 0.1%
531553 3
 
< 0.1%
525712 3
 
< 0.1%
536306 3
 
< 0.1%
Other values (4073) 4390
47.4%
(Missing) 4830
52.2%
ValueCountFrequency (%)
500025 1
< 0.1%
503083 1
< 0.1%
503588 1
< 0.1%
504060 1
< 0.1%
504689 1
< 0.1%
504700 1
< 0.1%
504706 1
< 0.1%
504742 1
< 0.1%
504899 1
< 0.1%
504901 1
< 0.1%
ValueCountFrequency (%)
571394 1
< 0.1%
563705 1
< 0.1%
559664 1
< 0.1%
558934 1
< 0.1%
558677 1
< 0.1%
558421 1
< 0.1%
558218 1
< 0.1%
556811 1
< 0.1%
556717 1
< 0.1%
556571 2
< 0.1%

Northing_m
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4017
Distinct (%)90.8%
Missing4830
Missing (%)52.2%
Infinite0
Infinite (%)0.0%
Mean180775.06
Minimum157050
Maximum200728
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size72.4 KiB
2023-04-22T13:08:10.302399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum157050
5-th percentile165339.75
Q1174906.5
median181149
Q3187178.5
95-th percentile194168
Maximum200728
Range43678
Interquartile range (IQR)12272

Descriptive statistics

Standard deviation8726.6951
Coefficient of variation (CV)0.048273777
Kurtosis-0.48870432
Mean180775.06
Median Absolute Deviation (MAD)6180.5
Skewness-0.24399832
Sum8.0011043 × 108
Variance76155208
MonotonicityNot monotonic
2023-04-22T13:08:10.427289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
191773 4
 
< 0.1%
194968 4
 
< 0.1%
192312 4
 
< 0.1%
178797 4
 
< 0.1%
181250 4
 
< 0.1%
191851 4
 
< 0.1%
192250 3
 
< 0.1%
184504 3
 
< 0.1%
169250 3
 
< 0.1%
180723 3
 
< 0.1%
Other values (4007) 4390
47.4%
(Missing) 4830
52.2%
ValueCountFrequency (%)
157050 1
< 0.1%
157070 1
< 0.1%
157160 1
< 0.1%
157377 1
< 0.1%
157490 1
< 0.1%
157656 1
< 0.1%
157759 1
< 0.1%
157793 1
< 0.1%
157837 1
< 0.1%
157951 1
< 0.1%
ValueCountFrequency (%)
200728 1
< 0.1%
200488 1
< 0.1%
199933 1
< 0.1%
199885 1
< 0.1%
199796 1
< 0.1%
199680 1
< 0.1%
199679 1
< 0.1%
199624 1
< 0.1%
199591 1
< 0.1%
199496 1
< 0.1%

Easting_rounded
Real number (ℝ)

Distinct530
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean531164.07
Minimum500050
Maximum571350
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size72.4 KiB
2023-04-22T13:08:10.554373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum500050
5-th percentile512550
Q1524650
median531550
Q3537675
95-th percentile548975
Maximum571350
Range71300
Interquartile range (IQR)13025

Descriptive statistics

Standard deviation10521.742
Coefficient of variation (CV)0.019808836
Kurtosis-0.21241279
Mean531164.07
Median Absolute Deviation (MAD)6600
Skewness-0.089793378
Sum4.9164546 × 109
Variance1.1070706 × 108
MonotonicityNot monotonic
2023-04-22T13:08:10.677331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
534350 58
 
0.6%
533850 57
 
0.6%
532850 57
 
0.6%
533150 55
 
0.6%
531350 55
 
0.6%
534850 53
 
0.6%
530650 52
 
0.6%
531850 52
 
0.6%
534150 52
 
0.6%
529150 49
 
0.5%
Other values (520) 8716
94.2%
ValueCountFrequency (%)
500050 1
 
< 0.1%
503050 1
 
< 0.1%
503550 1
 
< 0.1%
504050 1
 
< 0.1%
504650 1
 
< 0.1%
504750 3
 
< 0.1%
504850 1
 
< 0.1%
504950 9
0.1%
505050 4
< 0.1%
505150 5
0.1%
ValueCountFrequency (%)
571350 1
< 0.1%
563750 1
< 0.1%
559650 1
< 0.1%
558950 1
< 0.1%
558650 1
< 0.1%
558450 1
< 0.1%
558250 1
< 0.1%
556850 1
< 0.1%
556750 1
< 0.1%
556550 2
< 0.1%

Northing_rounded
Real number (ℝ)

Distinct425
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180791.57
Minimum157050
Maximum200750
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size72.4 KiB
2023-04-22T13:08:10.817595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum157050
5-th percentile166150
Q1175350
median181350
Q3186750
95-th percentile193450
Maximum200750
Range43700
Interquartile range (IQR)11400

Descriptive statistics

Standard deviation8286.6383
Coefficient of variation (CV)0.045835313
Kurtosis-0.41975771
Mean180791.57
Median Absolute Deviation (MAD)5700
Skewness-0.24369355
Sum1.6734068 × 109
Variance68668374
MonotonicityNot monotonic
2023-04-22T13:08:10.937549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
181050 59
 
0.6%
184050 57
 
0.6%
178750 56
 
0.6%
184750 56
 
0.6%
181850 55
 
0.6%
183550 55
 
0.6%
191850 55
 
0.6%
182550 55
 
0.6%
184550 53
 
0.6%
184650 52
 
0.6%
Other values (415) 8703
94.0%
ValueCountFrequency (%)
157050 2
< 0.1%
157150 1
 
< 0.1%
157350 1
 
< 0.1%
157450 2
< 0.1%
157650 1
 
< 0.1%
157750 4
< 0.1%
157850 1
 
< 0.1%
157950 2
< 0.1%
158050 2
< 0.1%
158150 1
 
< 0.1%
ValueCountFrequency (%)
200750 1
 
< 0.1%
200450 1
 
< 0.1%
199950 1
 
< 0.1%
199850 1
 
< 0.1%
199750 1
 
< 0.1%
199650 3
< 0.1%
199550 2
< 0.1%
199450 1
 
< 0.1%
199350 4
< 0.1%
199250 3
< 0.1%

Latitude
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct4357
Distinct (%)98.4%
Missing4830
Missing (%)52.2%
Infinite0
Infinite (%)0.0%
Mean51.487328
Minimum0
Maximum51.688304
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size72.4 KiB
2023-04-22T13:08:11.181668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile51.370879
Q151.457319
median51.51458
Q351.568074
95-th percentile51.630662
Maximum51.688304
Range51.688304
Interquartile range (IQR)0.11075444

Descriptive statistics

Standard deviation1.0976442
Coefficient of variation (CV)0.021318725
Kurtosis2188.0906
Mean51.487328
Median Absolute Deviation (MAD)0.055077306
Skewness-46.668851
Sum227882.91
Variance1.2048227
MonotonicityNot monotonic
2023-04-22T13:08:11.307705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51.60830546 4
 
< 0.1%
51.636755 4
 
< 0.1%
51.44517473 3
 
< 0.1%
51.4796965 3
 
< 0.1%
51.34293642 3
 
< 0.1%
51.66993903 3
 
< 0.1%
51.61948023 3
 
< 0.1%
51.37212223 3
 
< 0.1%
51.57131936 3
 
< 0.1%
51.5776778 2
 
< 0.1%
Other values (4347) 4395
47.5%
(Missing) 4830
52.2%
ValueCountFrequency (%)
0 2
< 0.1%
51.2976946 1
< 0.1%
51.29769841 1
< 0.1%
51.29826323 1
< 0.1%
51.30055143 1
< 0.1%
51.30071251 1
< 0.1%
51.30080578 1
< 0.1%
51.30111451 1
< 0.1%
51.30153402 1
< 0.1%
51.30223244 1
< 0.1%
ValueCountFrequency (%)
51.68830354 1
< 0.1%
51.687082 1
< 0.1%
51.68239033 1
< 0.1%
51.68114337 1
< 0.1%
51.68057949 1
< 0.1%
51.68046997 1
< 0.1%
51.67905963 1
< 0.1%
51.67889115 1
< 0.1%
51.67854795 1
< 0.1%
51.67852174 1
< 0.1%

Longitude
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4357
Distinct (%)98.4%
Missing4830
Missing (%)52.2%
Infinite0
Infinite (%)0.0%
Mean-0.11160719
Minimum-0.55973019
Maximum0.46642091
Zeros2
Zeros (%)< 0.1%
Negative3408
Negative (%)36.8%
Memory size72.4 KiB
2023-04-22T13:08:11.434459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.55973019
5-th percentile-0.38956067
Q1-0.21581008
median-0.1034015
Q3-0.013465014
95-th percentile0.16194826
Maximum0.46642091
Range1.0261511
Interquartile range (IQR)0.20234507

Descriptive statistics

Standard deviation0.16118811
Coefficient of variation (CV)-1.4442449
Kurtosis-0.30861714
Mean-0.11160719
Median Absolute Deviation (MAD)0.10249403
Skewness-0.071378615
Sum-493.9734
Variance0.025981605
MonotonicityNot monotonic
2023-04-22T13:08:11.557675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.0332283633 4
 
< 0.1%
-0.0165197536 4
 
< 0.1%
-0.2648413679 3
 
< 0.1%
-0.1511711545 3
 
< 0.1%
-0.0465480668 3
 
< 0.1%
-0.0688684854 3
 
< 0.1%
0.2258273817 3
 
< 0.1%
-0.1020306335 3
 
< 0.1%
-0.1676003509 3
 
< 0.1%
-0.2848974205 2
 
< 0.1%
Other values (4347) 4395
47.5%
(Missing) 4830
52.2%
ValueCountFrequency (%)
-0.5597301939 1
< 0.1%
-0.5167788377 1
< 0.1%
-0.5100463843 1
< 0.1%
-0.4985791082 1
< 0.1%
-0.49417632 1
< 0.1%
-0.4936804492 1
< 0.1%
-0.4910877461 1
< 0.1%
-0.4910247444 1
< 0.1%
-0.489683853 1
< 0.1%
-0.4895100345 1
< 0.1%
ValueCountFrequency (%)
0.4664209089 1
< 0.1%
0.3596292514 1
< 0.1%
0.3019354691 1
< 0.1%
0.2914238117 1
< 0.1%
0.2881475584 1
< 0.1%
0.2849650905 1
< 0.1%
0.2814149491 1
< 0.1%
0.2611464995 1
< 0.1%
0.2600524645 1
< 0.1%
0.2581075304 2
< 0.1%

Interactions

2023-04-22T13:08:03.327364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:48.030114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:49.361051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:50.768361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:52.069648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:53.582495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:54.978317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:56.456855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:57.814240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:59.087619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:00.613014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:01.904276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:03.433095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:48.147418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:49.469216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:50.873927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:52.183664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:53.697667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:55.087394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:56.567938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:57.924135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:59.207269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:00.721093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:02.008107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:03.541252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:48.247318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:49.573005image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:50.979928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:52.299093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:53.810250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:55.200637image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:56.682708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:58.026545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:59.322649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:00.831149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:02.113467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:03.642606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:48.361443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:49.680252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:51.087891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:52.413865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:53.924243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:55.315356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:56.792754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:58.127171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:59.569948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:00.933261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:02.232234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:03.759631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:48.481530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:49.903309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:51.206662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:52.531442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:54.036112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:55.433392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:56.907559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:58.237529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:59.691265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:01.047522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:02.356752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:03.877738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:48.599998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:50.017598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:51.322587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:52.654404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:54.154644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:55.549809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:57.027416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:58.355749image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:59.813147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:01.158835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:02.470832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:03.987329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:48.713120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:50.128400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:51.437651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:52.772080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:54.287748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:55.657581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:57.150606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:58.465860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:59.927298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:01.271337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:02.580919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:04.103860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:48.826090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:50.237378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:51.551223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:52.889610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:54.407296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:55.776822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:57.268676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:58.575935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:00.047601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:01.377490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:02.817260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:04.204892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:48.931967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:50.337522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:51.652050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:53.129890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:54.520051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:55.884349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:57.369501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:58.672681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:00.157488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:01.488219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:02.921438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:04.314973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:49.048465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:50.455822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:51.763361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:53.247548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:54.643353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:56.001378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:57.491405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:58.787509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:00.278312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:01.597354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:03.032591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:04.421341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:49.150596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:50.556927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:51.864182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:53.357448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:54.754173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:56.107526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:57.600013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:58.887330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:00.388764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:01.702675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:03.132587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:04.527489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:49.253808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:50.659744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:51.967024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:53.467749image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:54.866229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:56.350825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:57.708100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:07:58.987615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:00.497335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:01.802431image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-22T13:08:03.230186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-04-22T13:08:11.687534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
CalYearPumpHoursTotalHourlyNotionalCost(£)IncidentNotionalCost(£)UPRNUSRNEasting_mNorthing_mEasting_roundedNorthing_roundedLatitudeLongitudeFinYearPumpCountAnimalGroupParentOriginofCallPropertyCategorySpecialServiceTypeCategorySpecialServiceTypeBoroughCodeBorough
CalYear1.000-0.0040.9870.773-0.056-0.004-0.0190.014-0.0240.0160.014-0.0180.8690.0250.0620.1270.0360.1140.2000.0210.314
PumpHoursTotal-0.0041.000-0.0060.5610.010-0.0170.0730.0120.0390.0110.0090.0730.0020.4950.1300.0480.0790.1270.1440.0450.049
HourlyNotionalCost(£)0.987-0.0061.0000.782-0.058-0.005-0.0230.016-0.0260.0160.016-0.0221.0000.0180.0830.1500.0400.1000.2420.0130.423
IncidentNotionalCost(£)0.7730.5610.7821.000-0.031-0.0110.0320.0220.0050.0190.0210.0330.0150.4500.1200.0390.0740.1120.1430.0370.041
UPRN-0.0560.010-0.058-0.0311.0000.1840.129-0.1620.129-0.162-0.1640.1240.0850.0000.0910.0730.0160.0640.1010.6080.618
USRN-0.004-0.017-0.005-0.0110.1841.000-0.003-0.1550.043-0.166-0.157-0.0060.0080.0870.3830.1190.0770.0730.1580.9250.923
Easting_m-0.0190.073-0.0230.0320.129-0.0031.0000.1141.0000.1140.0810.9990.0000.1060.2430.0760.0270.1090.1240.7270.725
Northing_m0.0140.0120.0160.022-0.162-0.1550.1141.0000.1141.0000.9990.1340.0270.0660.1300.0560.0450.0920.1110.6300.628
Easting_rounded-0.0240.039-0.0260.0050.1290.0431.0000.1141.0000.1040.0810.9990.0000.0930.2340.0590.0480.0960.1080.7330.731
Northing_rounded0.0160.0110.0160.019-0.162-0.1660.1141.0000.1041.0000.9990.1340.0160.0530.0940.0480.0470.0710.0780.6380.638
Latitude0.0140.0090.0160.021-0.164-0.1570.0810.9990.0810.9991.0000.1010.0000.0000.2380.0000.0230.0000.0000.0200.000
Longitude-0.0180.073-0.0220.0330.124-0.0060.9990.1340.9990.1340.1011.0000.0000.1060.2420.0750.0270.1100.1240.7270.725
FinYear0.8690.0021.0000.0150.0850.0080.0000.0270.0000.0160.0000.0001.0000.0310.0560.1300.0380.1190.1670.0160.259
PumpCount0.0250.4950.0180.4500.0000.0870.1060.0660.0930.0530.0000.1060.0311.0000.1290.0400.0700.1360.1690.0440.057
AnimalGroupParent0.0620.1300.0830.1200.0910.3830.2430.1300.2340.0940.2380.2420.0560.1291.0000.1830.1720.2830.3290.1560.161
OriginofCall0.1270.0480.1500.0390.0730.1190.0760.0560.0590.0480.0000.0750.1300.0400.1831.0000.0460.0910.1100.0590.144
PropertyCategory0.0360.0790.0400.0740.0160.0770.0270.0450.0480.0470.0230.0270.0380.0700.1720.0461.0000.2260.2060.0810.091
SpecialServiceTypeCategory0.1140.1270.1000.1120.0640.0730.1090.0920.0960.0710.0000.1100.1190.1360.2830.0910.2261.0000.9990.1380.155
SpecialServiceType0.2000.1440.2420.1430.1010.1580.1240.1110.1080.0780.0000.1240.1670.1690.3290.1100.2060.9991.0000.1370.160
BoroughCode0.0210.0450.0130.0370.6080.9250.7270.6300.7330.6380.0200.7270.0160.0440.1560.0590.0810.1380.1371.0000.998
Borough0.3140.0490.4230.0410.6180.9230.7250.6280.7310.6380.0000.7250.2590.0570.1610.1440.0910.1550.1600.9981.000

Missing values

2023-04-22T13:08:04.727778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-22T13:08:05.347522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-22T13:08:05.697306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IncidentNumberDateTimeOfCallCalYearFinYearTypeOfIncidentPumpCountPumpHoursTotalHourlyNotionalCost(£)IncidentNotionalCost(£)FinalDescriptionAnimalGroupParentOriginofCallPropertyTypePropertyCategorySpecialServiceTypeCategorySpecialServiceTypeWardCodeWardBoroughCodeBoroughStnGroundNameUPRNStreetUSRNPostcodeDistrictEasting_mNorthing_mEasting_roundedNorthing_roundedLatitudeLongitude
01390912009-01-01 03:01:0020092008/09Special Service1.02.0255510.0RedactedDogPerson (land line)House - single occupancyDwellingOther animal assistanceAnimal assistance involving livestock - Other actionE05011467Crystal Palace & Upper NorwoodE09000008CroydonNorburyNaNWaddington Way20500146.0SE19NaNNaN532350170050NaNNaN
12750912009-01-01 08:51:0020092008/09Special Service1.01.0255255.0RedactedFoxPerson (land line)RailingsOutdoor StructureOther animal assistanceAnimal assistance involving livestock - Other actionE05000169WoodsideE09000008CroydonWoodsideNaNGrasmere RoadNaNSE25534785.0167546.053475016755051.390954-0.064167
220750912009-01-04 10:07:0020092008/09Special Service1.01.0255255.0RedactedDogPerson (mobile)Pipe or drainOutdoor StructureAnimal rescue from below groundAnimal rescue from below ground - Domestic petE05013756Carshalton CentralE09000029SuttonWallingtonNaNMill LaneNaNSM5528041.0164923.052805016495051.368941-0.161985
328720912009-01-05 12:27:0020092008/09Special Service1.01.0255255.0RedactedHorsePerson (mobile)Intensive Farming Sheds (chickens, pigs etc)Non ResidentialAnimal rescue from waterAnimal rescue from water - Farm animalE05013568Harefield VillageE09000017HillingdonRuislip1.000215e+11Park Lane21401484.0UB9504689.0190685.050465019065051.605283-0.489684
435530912009-01-06 15:23:0020092008/09Special Service1.01.0255255.0RedactedRabbitPerson (mobile)House - single occupancyDwellingOther animal assistanceAnimal assistance involving livestock - Other actionE05013971GooshaysE09000016HaveringHarold HillNaNSwindon Lane21300122.0RM3NaNNaN554650192350NaNNaN
537420912009-01-06 19:30:0020092008/09Special Service1.01.0255255.0RedactedUnknown - Heavy Livestock AnimalPerson (land line)House - single occupancyDwellingOther animal assistanceAnimal assistance involving livestock - Other actionE05014054AlibonE09000002Barking and DagenhamDagenhamNaNRogers Road19900321.0RM10NaNNaN549350184950NaNNaN
640110912009-01-07 06:29:0020092008/09Special Service1.01.0255255.0RedactedDogPerson (land line)ParkOutdoorOther animal assistanceAnimal assistance involving livestock - Other actionE05013883CathallE09000031Waltham ForestLeytonstoneNaNHolloway RoadNaNE11539013.0186162.053905018615051.5572210.003880
742110912009-01-07 11:55:0020092008/09Special Service1.01.0255255.0RedactedDogPerson (mobile)Lake/pond/reservoirOutdoorAnimal rescue from waterAnimal rescue from water - Domestic petE05000515WansteadE09000026RedbridgeLeytonstoneNaNAldersbrook RoadNaNE12541327.0186654.054135018665051.5610670.037434
843060912009-01-07 13:48:0020092008/09Special Service1.01.0255255.0RedactedSquirrelPerson (land line)House - single occupancyDwellingAnimal rescue from heightWild animal rescue from heightE05011470New Addington NorthE09000008CroydonAddingtonNaNBrockham Crescent20501673.0CR0NaNNaN538750163350NaNNaN
947150912009-01-07 21:24:0020092008/09Special Service1.01.0255255.0RedactedDogPerson (mobile)River/canalOutdoorAnimal rescue from waterAnimal rescue from water - Domestic petE05009380Lea BridgeE09000012HackneyStoke NewingtonNaNSouthwold RoadNaNE5535425.0186743.053545018675051.563314-0.047621
IncidentNumberDateTimeOfCallCalYearFinYearTypeOfIncidentPumpCountPumpHoursTotalHourlyNotionalCost(£)IncidentNotionalCost(£)FinalDescriptionAnimalGroupParentOriginofCallPropertyTypePropertyCategorySpecialServiceTypeCategorySpecialServiceTypeWardCodeWardBoroughCodeBoroughStnGroundNameUPRNStreetUSRNPostcodeDistrictEasting_mNorthing_mEasting_roundedNorthing_roundedLatitudeLongitude
9246027463-240220232023-02-24 10:57:0020232022/23Special Service1.01.0364364.0CAT STUCK IN TREE - BEEN A FEW DAYS RSPCA NOT ATTENDING CALLER IS NOT ON SCENECatPerson (mobile)Domestic garden (vegetation not equipment)OutdoorAnimal rescue from heightAnimal rescue from height - Domestic petE05014066NORTHBURYE09000002BARKING AND DAGENHAMBarking1.000625e+08WHITING AVENUE19900757.0IG11543939.0184264.054395018425051.5389330.074120
9247027587-240220232023-02-24 15:00:0020232022/23Special Service1.01.0364364.0CAT STUCK IN TREE- ANIMAL WELFARE ON SCENECatPerson (mobile)Tree scrubOutdoorAnimal rescue from heightAnimal rescue from height - Domestic petE05014066NORTHBURYE09000002BARKING AND DAGENHAMBarking1.000624e+08WHITING AVENUE19900757.0IG11543894.0184282.054385018425051.5391130.073479
9248027655-240220232023-02-24 17:34:0020232022/23Special Service1.01.0364364.0KITTEN ON THE ROOFCatPerson (mobile)House - single occupancyDwellingAnimal rescue from heightAnimal rescue from height - Domestic petE05013569HAYES TOWNE09000017HILLINGDONHayesNaNCOLDHARBOUR LANE21400437.0UB3NaNNaN510150180350NaNNaN
9249027745-240220232023-02-24 20:25:0020232022/23Special Service1.01.0364364.0INJURED CAT STUCK IN CRAWL SPACE IN BASEMENTCatPerson (mobile)House - single occupancyDwellingOther animal assistanceAnimal harm involving domestic animalE05014100CLAPHAM EASTE09000022LAMBETHClaphamNaNKENWYN ROAD21900794.0SW4NaNNaN529850175350NaNNaN
9250028192-250220232023-02-25 18:26:0020232022/23Special Service1.01.0364364.0CAT STUCK IN CAR ENGINECatPerson (mobile)CarRoad VehicleOther animal assistanceAnimal assistance involving domestic animal - Other actionE05013762ST. HELIER WESTE09000029SUTTONMitcham5.870055e+09KELSO ROAD22600673.0SM5526425.0166689.052645016665051.385180-0.184559
9251028245-250220232023-02-25 20:04:0020232022/23Special Service1.01.0364364.0CAT STUCK IN TREE - BEEN STUCK FOR A FEW DAYSCatPerson (mobile)Tree scrubOutdoorAnimal rescue from heightAnimal rescue from height - Domestic petE05013561WEALDSTONE NORTHE09000015HARROWStanmore1.000213e+11THE MEADOW WAY21202458.0HA3515393.0190658.051535019065051.602959-0.335183
9252028537-260220232023-02-26 11:44:0020232022/23Special Service1.01.0364364.0FOX TRAPPED IN FOLIAGE - BEHIND PROPERTY ON PATIO AREAFoxPerson (mobile)Domestic garden (vegetation not equipment)OutdoorOther animal assistanceAssist trapped wild animalE05009400PEMBRIDGEE09000020KENSINGTON AND CHELSEANorth Kensington2.170913e+08WESTBOURNE GROVE21701056.0W11525094.0181044.052505018105051.514489-0.198595
9253028818-260220232023-02-26 23:00:0020232022/23Special Service1.01.0364364.0INDOOR CAT ESCAPED STUCK ON ROOF - THREE FLOOR BUILDINGCatPerson (mobile)Purpose Built Flats/Maisonettes - Up to 3 storeysDwellingAnimal rescue from heightAnimal rescue from height - Domestic petE05013602TOTTENHAM HALEE09000014HARINGEYTottenhamNaNPARK VIEW ROAD21104904.0N17NaNNaN534650190250NaNNaN
9254029192-270220232023-02-27 17:38:0020232022/23Special Service1.01.0364364.0RedactedBirdPerson (land line)Fire stationNon ResidentialOther animal assistanceAnimal assistance involving wild animal - Other actionE05014117VAUXHALLE09000022LAMBETHLambeth1.000232e+11ALBERT EMBANKMENT21900079.0SE1530523.0178782.053055017875051.492927-0.121245
9255029604-280220232023-02-28 15:19:0020232022/23Special Service1.01.0364364.0RedactedCatPerson (mobile)Tree scrubOutdoorAnimal rescue from heightAnimal rescue from height - Domestic petE05013738FULHAM REACHE09000013HAMMERSMITH AND FULHAMFulham3.402301e+07EVERINGTON STREET21000323.0W6523883.0177751.052385017775051.485158-0.217199